Building automation system with edge processing diversity

ABSTRACT

A rooftop unit includes a housing, air conditioning components coupled to the housing, and circuitry enclosed within and/or coupled to the housing and programmed to execute a control logic for the air conditioning components, an expression-based event processing logic, and a machine learning algorithm.

CROSS-REFERENCE TO RELATED APPLICATION

This application the benefit of and priority to U.S. ProvisionalApplication No. 63/315,442, filed Mar. 1, 2022, the entire disclosure ofwhich is incorporated by reference herein.

BACKGROUND

The present disclosure relates generally to building equipment andbuilding automation systems. Conventional building automation systemsinclude complex architectures that can require a wide number of devices,supervisory and field controllers, on-premises servers, off-premisesservers, and other infrastructure to be manually configured by fieldtechnicians in order to establish and maintain a building automationsystem. Such architectures may be suitable for large scale facilitiesbut substantially different in structure than building automationsystems for smaller scale entities, leading to extra complexity (e.g.,driven by higher numbers of different tools and platforms) andpreventing or complicating comparisons across facilities of differenttype and size. Additionally, each of such building devices may havelimited functionality or execute limited types of logic and diversedevices, including different equipment, sensors, data sources, etc.(including within a given building), often use different protocols anddata formats which create barriers to integrations and reduceinteroperability between building devices. The present disclosureaddresses these and other challenges.

SUMMARY

One implementation of the present disclosures is a rooftop unitincluding a housing, air conditioning components coupled to the housing,and circuitry enclosed within and/or coupled to the housing andprogrammed to execute a control logic for the air conditioningcomponents, an expression-based event processing logic, and a machinelearning algorithm.

In some embodiments, the expression-based event processing logicperforms pattern recognition for data received by the circuitry from theair conditioning components or one or more external data sources. Insome embodiments, the machine learning algorithm is based on a machinelearning model trained at a cloud system remote from the rooftop unit.In some embodiments, the machine learning algorithm includes a modifiedversion of the machine learning model trained at the cloud system thatis configured to execute on more limited processing resources of thecircuitry relative to the cloud system.

In some embodiments, the expression-based event processing logicdiagnoses occurring fault conditions and the machine learning algorithmpredicts future fault conditions. In some embodiments, the circuitry isprogrammed to modify the expression-based event processing logic inresponse to remote updates received at the circuitry. Theexpression-based event processing logic and the machine learningalgorithm may have a combined memory footprint of less than 256 MB.

In some embodiments, the rooftop unit of claim 1, wherein the circuitryreceives a first data set from the air condition components and a seconddata set from an external sensor, wherein the machine learning algorithmuses the first data set and the second data set as inputs. The externalsensor may be an indoor air quality sensor.

In some embodiments, the circuitry is configured to establishedcommunications with a cloud system and the control logic for the airconditioning components, the expression-based event processing logic,and the machine learning algorithm are functional during interruptionsof the communications with the cloud system.

In some embodiments, the control logic for the air conditioningcomponents is a native control logic and the expression-based eventprocessing logic includes a supplement or modification of the nativecontrol logic. The supplement or modification of the native controllogic may be received from the cloud system or another computing systemexternal from the rooftop unit via a network connection. The supplementor modification of the native control logic may be received afterinstallation of the rooftop unit while the rooftop unit is connected tothe network connection and operational.

In some embodiments, the control logic includes a first set of one ormore fault detection and/or diagnostics rules, and the expression-basedevent processing logic includes a second set of one or more faultdetection and/or diagnostics rules that supplement or modify the firstset of one or more fault detection and/or diagnostics rules. The secondset of one or more fault detection and/or diagnostics rules is receivedfrom the cloud system or another source remote from the rooftop unit anddefined according to an expression-based language.

Another implementation of the present disclosure is a unit of buildingequipment including a mechanical component controllable to affect acondition of a building and circuitry packaged with the mechanicalcomponent and programmed to execute a control logic for the heating,ventilation, or cooling component, an expression-based event processinglogic, and a machine learning algorithm.

In some embodiments. the expression-based event processing logicperforms pattern recognition for data received by the circuitry from themechanical component or one or more external data sources. The machinelearning algorithm may be based on a machine learning model trained at acloud system remote from the rooftop unit. The machine learningalgorithm may include a modified version of the machine learning modeltrained at the cloud system that is configured to execute on morelimited processing resources of the circuitry relative to the cloudsystem.

In some embodiments, the expression-based event processing logicdiagnoses occurring fault conditions and the machine learning algorithmpredicts future fault conditions. In some embodiments, the circuitry isprogrammed to modify the expression-based event processing logic inresponse to remote updates received at the circuitry. Theexpression-based event processing logic and the machine learningalgorithm may have a combined memory footprint of less than 256 MB.

In some embodiments, the circuitry receives a first data set from themechanical component and a second data set from an external sensor,wherein the machine learning algorithm uses the first data set and thesecond data set as inputs. The external sensor may be an indoor airquality sensor.

In some embodiments, the circuitry is configured to establishedcommunications with a cloud system and the control logic for themechanical component, the expression-based event processing logic, andthe machine learning algorithm are functional during interruptions ofthe communications with the cloud system.

In some embodiments, the control logic for the air conditioningcomponents is a native control logic. The expression-based eventprocessing logic may include a supplement or modification of the nativecontrol logic. The supplement or modification of the native controllogic may be received from the cloud system or another computing systemexternal from the unit via a network connection. The supplement ormodification of the native control logic can be received afterinstallation of the unit while the unit is connected to the networkconnection and operational.

In some embodiments, the control logic includes a first set of one ormore fault detection and/or diagnostics rules, where theexpression-based event processing logic includes a second set of one ormore fault detection and/or diagnostics rules that supplement or modifythe first set of one or more fault detection and/or diagnostics rules.The second set of one or more fault detection and/or diagnostics rulesmay be received from the cloud system or another source remote from theunit and defined according to an expression-based language.

Another implementation of the present disclosure is a system. The systemincludes, a unit of building equipment including a heating, ventilation,or cooling component, onboard circuitry configured to execute a controllogic for the heating, ventilation, or cooling component, anexpression-based event processing logic, and a machine learningalgorithm, and a cloud system communicably connectable to the onboardcircuitry and including circuitry. The circuitry of the cloud system isconfigured to transmit an expression to the onboard circuitry for use bythe expression-based event processing logic and transmit a machinelearning model to the onboard circuitry for use by the machine learningalgorithm.

In some embodiments, the expression-based event processing logicperforms pattern recognition for data received at the onboard circuitryfrom the heating, ventilation, or cooling component or an external datasource. In some embodiments, the cloud system is configured to generatethe machine learning model by training a neural network on a trainingdata set including historical data from at least one of the buildingequipment or other building equipment and generating the machinelearning model to transmit to the onboard circuitry using the trainedneural network. In some embodiments, the expression-based eventprocessing logic diagnoses occurring fault conditions and the machinelearning algorithm predicts future fault conditions. Theexpression-based event processing logic and the machine learningalgorithm may have a combined memory footprint of less than 256 MB.

In some embodiments, the system also includes a plurality of externaldata sources providing a plurality of data streams to the onboardcircuitry, where the expression-based even processing logic and themachine learning algorithm are adapted to use the plurality of datastreams as inputs. The control logic, the expression-based eventprocessing logic, and the machine learning algorithm may be fullyfunctional during interruptions of a connection between the onboardcircuitry and the cloud system.

Another implementation of the present disclosure is a method. The methodincludes providing a package including a heating, ventilation, orcooling component and onboard circuitry. The method also includesexecuting, by the onboard circuitry, control logic to control theheating, ventilation, or cooling component, executing, by the onboardcircuitry, an expression-based event processing logic, and executing, bythe onboard circuitry, a machine learning algorithm.

In some embodiments, the method further includes training, by acomputing system remote from the onboard circuitry, a neural network ona training data set includes historical data from at least one of theheating, ventilation, or cooling component or other heating,ventilation, or cooling components. The method may also includinggenerating the machine learning model to transmit to the onboardcircuitry using the trained neural network.

In some embodiments, executing the expression-based event processinglogic provides recognition of patterns in data received at the onboardcircuitry from the heating, ventilation, or cooling component or anotherdata source. Executing the expression-based event processing logic maydiagnose an occurring fault condition. Executing the machine learningalgorithm may predict a future fault condition.

In some embodiments, the method also includes receiving, at the onboardcircuitry and from a cloud system, a set of expressions and a machinelearning model. Executing the expression-based event processing logicincludes using the set of expressions and executing the machine learningmodel includes using the machine learning model.

Another implementation of the present disclosure is a rooftop unitincluding a housing, air conditioning components coupled to the housing,and onboard circuitry enclosed within and/or coupled to the housing andprogrammed to receive data from a plurality of data sources communicablycoupled to the onboard circuitry. The data is received in a plurality ofdifferent formats according to a plurality of different protocols. Thecircuitry is also programmed to translate the data from the plurality offormats into a common data format and perform operations onboard therooftop unit using the data in the common data format.

In some embodiments, the plurality of protocols include two or more ofMQTT, BACnet, Modbus, data distribution service (DDS), or OPC UnifiedArchitecture (OPCUA). In some embodiments, the plurality of data sourcesinclude two or more of a temperature sensor, and indoor air qualitysensor, an airflow sensor, a pressure sensor, an occupancy sensor, theair conditioning components, an additional rooftop unit, or a differenttype of building equipment. In some embodiments, the circuitry uses ananalytic expression language to translate the data from the plurality ofdata sources into the common data format. In some embodiments, thecircuitry translates the data in real time as the data streams to thecircuitry from the plurality of data sources.

In some embodiments, the common data format is a Brick format. Theonboard circuitry is configured to translate the data into the commondata format by applying tags to the data. The tags may correspond totypes of entities and types of relationships between the entities. Thetags may include location, event, asset, and place tags.

In some embodiments, the rooftop unit may include an onboardcomputer-readable storage medium. The circuitry may be configured toingest the data in the common data format into a locally-stored digitaltwin stored within the onboard computer-readable storage medium. In someembodiments, the circuitry is further configured to transmit the data inthe common data format to an off-premises computing system and cause thedata in the common data format to be stored in a digital twin at theoff-premises computing system.

In some embodiments, the circuitry is further configured to execute amachine learning model stored onboard the rooftop unit that uses thedata in the common data format as an input. In some embodiments, thecircuitry is further configured to filter, normalize, and align the datato facilitate use of the data as the input to the machine learningmodel. The circuitry may include a common data bus such that thecircuitry is configured to provide the data in the common data format onthe common data bus.

Another implementation of the present disclosure is a method. The methodincludes delivering and installing a package including a heating,ventilation, or cooling component and onboard circuitry, connecting theonboard circuitry to a plurality of data sources that use a plurality ofdata protocols, receiving, by the onboard circuitry, data from theplurality of data sources and translating by the onboard circuitry, thedata from the plurality of data protocols to a common data format inreal time. The method may also including affecting, by the onboardcircuitry, operation of the heating, ventilation, or cooling componentby executing an operation using the data in the common data format.

In some embodiments, the plurality of data protocols include two or moreof MQTT, BACnet, Modbus, data distribution service (DDS), or OPC UnifiedArchitecture (OPCUA). In some embodiments, the plurality of data sourcesinclude two or more of a temperature sensor, and indoor air qualitysensor, an airflow sensor, a pressure sensor, an occupancy sensor, theheating, cooling, or ventilation component, or a different type ofbuilding equipment.

In some embodiments, translating the data from the plurality of dataprotocols to a common data format in real time is performed using ananalytic expression language. In some embodiments, the common dataformat is a Brick format with location, event, asset, and place (LEAP)tags, and translating the data into the common data format includesapplying tags to the data. The tags may correspond to types of entitiesand types of relationships between the entities. The tags may includelocation, event, asset, and place tags.

In some embodiments, the method includes ingesting the data into adigital twin stored within an onboard computer-readable storage mediumcoupled to the heating, ventilation, or cooling component. The methodmay further include causing the data to be stored in a digital twinstored at an off-premises computing system communicable with the onboardcircuitry.

In some embodiments, the method also includes executing, by the onboardcircuitry, a machine learning model stored at the onboard circuitryusing the data in the common data format as an input. The method canalso include further including filtering, normalizing, and aligning, bythe onboard circuitry, the data to facilitate use of the data as theinput to the machine learning model.

Another implementation of the present disclosure is a system. The systemincludes a unit of building equipment including onboard circuitry, aplurality of data sources communicably coupled to the onboard circuitrysuch that data is received at the onboard circuitry from the pluralityof data sources in a plurality of different formats according to aplurality of different protocols. The onboard circuitry is configured totranslate the data from the plurality of data sources and associatedwith the plurality of data protocols into a common data set having acommon data format and locally execute logic relating to operation ofthe unit of building equipment using the common data set having thecommon data format.

In some embodiments, the logic includes one or more of feedback controllogic, expression-based pattern recognition logic, or a machine learningalgorithm.

Another implementation of the present disclosure is a unit of buildingequipment. The unit of building equipment includes onboard circuitryprogrammed to receive data from a plurality of data sources communicablycoupled to the onboard circuitry, the data received in a plurality ofdifferent formats according to a plurality of different protocols,translate the data from the plurality of formats into a common dataformat, and perform operations onboard the unit of building equipmentusing the data in the common data format.

Another implementation of the present disclosure is a rooftop unit. Therooftop unit includes a housing, air conditioning components coupled tothe housing, and onboard circuitry enclosed within and/or coupled to thehousing and including inputs configured to receive signals from aplurality of sources communicably coupled to the onboard circuitry. Theonboard circuitry is programmed to execute a configuration routinestored on the circuitry configured to automatically configure operatingparameters of the rooftop unit based on the signals received via theinputs.

In some embodiments, the configuration routine includes patternrecognition performed using an expression-based processing language. Insome embodiments, the configuration routine uses a machine learningmodel stored and executed on the rooftop unit. The configuration routinemay configured to identify devices associated with the signals.Automatically configuring the parameters may be based on identities ofthe devices. Automatically configuring the parameters may be based onpoint values represented by the signals. Automatically configuring theparameters may be based on a plurality of data protocols used by theplurality of sources.

In some embodiments, executing the configuration routine includesexecuting a first routine on the circuitry of the rooftop unit, andresponsive to triggering of a condition of the first routine, causingexecution of a second routine by a cloud system communicable with thecircuitry and receiving at least a portion of a configuration of theoperating parameters from the cloud system in response.

In some embodiments, the onboard circuitry is communicable with a cloudsystem and receives an additional parameter for the rooftop unit fromthe cloud system. The configuration routine may be trained based on ahistory of configurations of other rooftop units. The configurationroutine may be trained based on a history of configurations of otherrooftop units and data relating to other devices in or on a samebuilding or space as the other rooftop units.

In some embodiments, the onboard circuitry is configured to detect anaddition or removal of a source of the plurality of sources and theconfiguration routine is configured to update the parameters in responseto the addition or removal. In some embodiments, the onboard circuitryis configured to detect a trend in a value of a point provided by afirst source of the plurality of sources, and the configuration routineis configured to adjust a first parameter of the parameters based on thetrend.

In some embodiments, the circuitry is configured to execute theconfiguration routine to adjust a first parameter of the parametersbased on comparison of or a trend in values of a plurality of pointsreceived from two or more different sources of the plurality of sources.In some embodiments, the plurality of sources include local sourceslocated at a same facility as the rooftop unit, internal sources at orproximate the rooftop unit, peer sources communicating in a peer-to-peerfashion with the onboard circuitry, or external sources communicatingdirectly with the onboard circuitry or only via devices located at thesame facility.

Another implementation of the present disclosure is a system. The systemincludes a unit of building equipment serving a building and including aheating, ventilation, or cooling component and onboard circuitryconfigured to execute a configuration routine stored on the circuitrywhich automatically configures parameters of the rooftop unit based onthe signals received from sensors and devices at the building. Thesystem also includes a cloud system communicably connectable to theonboard circuitry and configured to influence the configuration routine.

In some embodiments, the cloud system is configured to influence theconfiguration routine by training a machine learning model based onhistorical configurations and providing a result of the training to theonboard circuitry for use in the configuration routine. In someembodiments, the cloud system is configured to create a modified versionof the machine learning model trained at the cloud system that isconfigured to execute on more limited processing resources of thecircuitry relative to the cloud system and provide the modified versionto the onboard circuitry.

In some embodiments, the cloud system is configured to influence theconfiguration routine by training a machine learning algorithm onhistorical configurations, using the machine learning algorithm todetermine a set of rule-based or expression-based logic, and pushing theset of rule-based or expression-based logic to the onboard circuitry forexecution by the onboard circuitry as a part of the configurationroutine. In some embodiments, the cloud system is configured toinfluence the configuration routine by configuring additional parametersof rooftop unit.

In some embodiments, the onboard circuitry is further configured toprovide the parameters to the cloud system, and wherein the cloud systemstores the parameters in a digital twin. In some embodiments, theonboard circuitry is further configured to store a digital twin andintegrate the parameters into the digital twin.

Another implementation of the present disclosure is a method. The methodincludes providing a package including a heating, ventilation, orcooling component and onboard circuitry, connecting the onboardcircuitry to a plurality of data sources and to a cloud system,detecting, by the onboard circuitry, characteristics of data receivedfrom the plurality of data sources and forwarding the characteristics tothe cloud system, generating, by the cloud system, a configuration foruse by the onboard circuitry in managing operations of the heating,ventilation, or cooling component, and automatically configuring theonboard circuitry in accordance with the configuration from the cloudsystem.

In some embodiments, generating the configuration includes using thecharacteristics as inputs to a machine learning model trained onhistorical configurations for other building equipment communicable withthe cloud system. In some embodiments, the plurality of sources includelocal sources located at a same facility as the rooftop unit, internalsources at or proximate the rooftop unit, peer sources communicating ina peer-to-peer fashion with the onboard circuitry, or external sourcescommunicating directly with the onboard circuitry or only via deviceslocated at the same facility.

Another implementation of the present disclosure is a unit of buildingequipment. The unit of building equipment includes onboard circuitryincluding inputs configured to receive signals from a plurality ofsources communicably coupled to the onboard circuitry. The onboardcircuitry is programmed to execute a configuration routine stored on theonboard circuitry configured to automatically configure operatingparameters of the unit of building equipment based on the signalsreceived via the inputs.

Another implementation of the present disclosure is a system. The systemincludes a cloud system in communication with a first unit of buildingequipment and a second unit of building equipment. The cloud system isconfigured to aggregate event data from a first unit of buildingequipment at a first building including first onboard circuitryconfigured to perform first onboard event processing to generate theevent data for the first unit building equipment and a second unit ofbuilding equipment at a second building including second onboardcircuitry configured to configured to perform the second onboard eventprocessing to generate event data for the second unit buildingequipment. The cloud system is also configured to provide a userinterface including a view of the aggregated event data for the firstunit of building equipment and the second unit of building equipment.

In some embodiments, the first unit of building equipment is owned by afirst customer, the second unit of building equipment is owned by asecond customer, and the cloud system is configured to provide the userinterface to a distributor via a distributor portal. In someembodiments, the first building is a branch location and the secondbuilding is a headquarters location. In some embodiments, the firstbuilding is a retail location and the second building is a warehouse.

In some embodiments, the first onboard event processing includescalculating a first performance index value for the first unit and thesecond onboard event processing includes calculating a secondperformance index value for the second unit. The user interface candisplay the first performance index value and the second performanceindex value. In some embodiments, the first unit of building equipmentand the second unit of building equipment provides the event data to thecloud system in a common data format. The common data format mayidentify a location and an owner associated with a set of data.

In some embodiments, the first unit of building equipment is a rooftopunit and the second unit of building equipment is a chiller. In someembodiments, the event data includes a fault diagnosis.

In some embodiments, the cloud system is configured to receive eventdata generated by a third unit of building equipment at a thirdbuilding, aggregate the event data from the third unit with the eventdata from the first unit and the second unit, and apply a filtering ruleto omit the third data from the user interface including the aggregatedevent data for the first unit and the second unit. In some embodiments,the cloud system is configured to receive event data generated by athird unit of building equipment at a third building, aggregate theevent data from the third unit with the event data from the first unitand the second unit, and determine at least one of an order or a visualeffect for the presentation of the respective event data from the firstunit, the second unit, and the third unit based on characteristics ofthe event data.

Another implementation of the present disclosure is a method. The methodincludes aggregating event and performance data from a fleet of buildingequipment and displaying a visualization of the event and performancedata for the fleet of building equipment. The visualization includesoptions to filter the event and performance data based oncharacteristics of units in the fleet of building equipment, thecharacteristics including location, owner, lead technician, salesrepresentative, equipment type, equipment model, and/or ongoing orrecent events. The event and performance data is automaticallyassociated with the characteristics by the fleet of building equipment.

In some embodiments, the fleet of building equipment includes aplurality of types of equipment serving a plurality of types offacilities. The plurality of types of facilities may include branchlocations and a headquarters campus. The plurality of types offacilities may include storefronts and warehouses.

In some embodiments, the visualization further includes options to rankthe units in the fleet of building equipment based on values of aperformance metric calculated locally on the units of the fleet ofbuilding equipment. In some embodiments, the fleet of building equipmentserves customers of a distributor and wherein the method includesdisplaying the visualization to the distributor.

In some embodiments, the method also includes generating, locally oncircuitry of a unit of building equipment, a recommended interventionand displaying the recommended intervention with the visualization.Generating the recommended intervention may include predicting a faultfor the unit of building equipment.

Another implementation of the present disclosures is a rooftop unit. Therooftop unit includes a housing, air conditioning components coupled tothe housing, and circuitry enclosed within and/or coupled to thehousing. The circuitry is configured to generate event and performancedata for the rooftop unit based on data relating to operation of the airconditioning components and using one or both of expression-basedpattern event processing and a machine learning algorithm. The circuitryis also configured to transmit the event and performance data to adistributor portal for display alongside event and performance data foradditional rooftop units, wherein the circuitry is configured to providethe event and performance data in a standard format that identifies alocation and identity of the rooftop unit.

In some embodiments, the event and performance data includes a faultdiagnosis. In some embodiments, the event and performance data includesa performance index score calculated based on a number of faultsidentified in the event and performance data.

Another implementation of the present disclosure is unit of buildingequipment including onboard circuitry programmed to generate event andperformance data for the unit of building equipment based on datarelating to operation of the unit of building equipment and using one orboth of expression-based pattern event processing and a machine learningalgorithm. The onboard circuitry is also configured to transmit theevent and performance data to a distributor portal for display alongsideevent and performance data for other building equipment, wherein thecircuitry is configured to provide the event and performance data in astandard format that identifies a location and identity of the unit ofbuilding equipment.

Another implementation of the present disclosure is a system. The systemincludes a first unit of building equipment operable to provide heating,ventilation, or cooling to a building. The first unit of buildingequipment includes local processing circuitry integrated with the firstunit of building equipment. The system also includes a plurality ofadditional units of building equipment serving the building, where theplurality of additional units of building equipment are directlycommunicable with the local processing circuitry of the first unit ofbuilding equipment, and where the local processing circuitry isconfigured to perform a first set of analytics using data for the firstunit of building equipment and data from the plurality of additionalunits of building equipment. The system also includes an off-premisescomputing system located remotely from the building and communicabledirectly with the local processing circuitry. The off-premises serverconfigured to perform a second set of analytics using the data for thefirst unit of building equipment and the data from the plurality ofadditional units of building equipment.

In some embodiments, the first unit of building equipment and theplurality of additional units of building equipment communicate directlyvia peer-to-peer communication. In some embodiments, the off-premisesserver is directly communicable with the first unit of buildingequipment via networking infrastructure without any intervening field orsupervisory controllers.

In some embodiments, the local processing circuitry includes a databridge component configured to facilitate communication of the data forthe first unit of building equipment and the data from the plurality ofadditional units of building equipment to the off-premise server. Insome embodiments, the first unit of building equipment, the plurality ofadditional units of building equipment, and the off-premises server arearranged in an architecture which is agnostic of a count of a type orsize of the building. The architecture may be further agnostic of acount of the plurality of additional units of building equipment andequipment types of the first unit of building equipment and theplurality of additional units of building equipment.

In some embodiments, the first set of analytics includesexpression-based event processing. In some embodiments, the first set ofanalytics includes using a machine learning algorithm.

In some embodiments, the system further includes a plurality of sensorsproviding sensor data to the local processing circuitry. The localprocessing circuitry may be further configured to standardize a dataformat of the sensor data, the data for the first unit of buildingequipment, and the data from the plurality of additional units ofbuilding equipment.

In some embodiments, the first unit of building equipment is a rooftopunit. The first unit of building equipment and the plurality ofadditional units of building equipment may be rooftop units. In someembodiments, the first unit of building equipment and the plurality ofadditional units are one or more types of building equipment selectedfrom a set including rooftop units, chillers, boilers, cooling towers,air handling units, variable air volume boxes, variable refrigerant flowoutdoor units, and variable refrigerant flow indoor units. In someembodiments, the plurality of additional units of building equipmentinclude central plant equipment.

Another implementation of the present disclosures is a system. Thesystem consists of a plurality of units building equipment operable todirectly affect one or more physical conditions of a building,communications network infrastructure, and an off-premise computercommunicable with the plurality of units of building equipment via thenetworking infrastructure. The plurality units of building equipmentinclude a plurality of processing units programmed to enable datapipelines between the plurality of units of building equipment and theoff-premise computer.

In some embodiments, the plurality of processing units are furtherprogrammed to locally executing expression-based event processing logic.The plurality of processing units may be further programmed to store anduse the machine learning models. In some embodiments, the plurality ofunits building equipment include lighting equipment and heating,ventilation, or cooling equipment.

Another implementation of the present disclosure is a unit of buildingequipment. The unit of building equipment includes a housing, heating,cooling, or ventilation components coupled to the housing, and onboardcircuitry enclosed within and/or coupled to the housing and programmedto communicate directly with both a plurality of additional units ofbuilding equipment and an off-premise computing system.

In some embodiments, the onboard circuitry is directly communicable withthe off-premise computing system via networking infrastructure withoutany intervening field or supervisory controllers. In some embodiments,the onboard circuitry includes a data bridge component configured tofacilitate communication of the data for the first unit of buildingequipment to the off-premise server.

Another implementation of the present disclosures is a method. Themethod includes providing a plurality of units building equipmentoperable to directly affect one or more physical conditions of abuilding, connecting the plurality of units of building equipment to acommunication network, and automatically establishing, by the pluralityof units of building equipment, communications between the plurality ofunits of building equipment and an off-premise server via thecommunication network. Such communications are not routed through anycontrollers external to the plurality of units of building equipment.

In some embodiments, the method also includes providing a hybridbuilding management service including first processing operationsexecuted locally on the plurality of units of building equipment andsecond processing operations executed on the off-premise server. In someembodiments, the method also includes automatically populating a digitaltwin with an arrangement of the plurality of units of building equipmentin response to establishment of the communications.

BRIEF DESCRIPTION OF THE FIGURES

The disclosure will become more fully understood from the followingdetailed description, taken in conjunction with the accompanyingfigures, wherein like reference numerals refer to like elements, inwhich:

FIG. 1 is a first block diagram of a building automation system,according to some embodiments.

FIG. 2 is a second block diagram of a building automation system,according to some embodiments.

FIG. 3 is a block diagram of edge circuitry and a cloud system of abuilding automation system, according to some embodiments.

FIG. 4 is an example illustration of a cloud manager interface formanaging logic executed by edge circuitry, according to someembodiments.

FIG. 5 is an example illustration of an events viewer interface showingevent alerts generated by the edge circuitry using logic managed by thecloud manager, according to some embodiments.

FIG. 6 is a first view in a distributor dashboard, according to someembodiments.

FIG. 7 is a second view in the distributor dashboard, according to someembodiments.

DETAILED DESCRIPTION

Referring generally to the figures, advances in building automationsystems are shown according to various embodiments. The advancesdescribed herein, in various embodiments, leverage multi-domainexpertise to embed intelligence and data at the edge, enable fasterdeployment of building equipment and building automation systems via aplug-and-play architecture, seamlessly share data and optimize multiplecompeting demands using the organized data, provide a scalablearchitecture enabled to work across different size buildings fromlarge/complex (e.g., hospital, headquarters) to light commercial (e.g.,retail storefront, small office), provide a hybrid architecture withon-premises capabilities and cloud readiness, are future-proofed byover-the-air update capabilities, provide high levels of cyber security,and/or provide an easy on-ramp for advanced digital features. Thebuilding automation systems herein may enable fixed function controls,configurable controls, pro-figurable functions, and/orprogrammable/fully-customizable functions in various embodiments and atvarious scales.

The present disclosure contemplates, in some embodiments,factory-shipping packaged control circuitry with a unit of buildingequipment (e.g., a rooftop unit, a chiller, etc.) (i.e., as anintegrated package, on one pallet, positioned in shared housing, etc.),where the onboard/integrated circuitry provides various advanced dataingestion, machine learning, expression-based pattern recognition,and/or other control and analytics functions as detailed below. Thepresent disclosure also contemplates retrofitting existing buildings orequipment by installing such circuitry with existing equipment, in someimplementations. Such circuitry may further included embedded cloudbridge technology and/or building twin technology to enable hybriddigital functionalities in seamless and direct interaction with cloudservices including cloud-based digital twins, edge-based digital twins,or a combination thereof. Such unification of advanced functionalitiesat the edge can drive local improvements for internal operation of aunit of building equipment, plug-and-play higher-order cloud analyticsand control optimizations, and automatic data standardization andaggregation enabling visualizations across facilities, owners,customers, and equipment types.

These and other features and advantages are described in further detailbelow with reference to the drawings.

Referring now to FIG. 1 , a building automation system 100 is shownaccording to some embodiments. The building automation system 100 isshown as including a cloud tier 102 and an on-premises tier 104, wherethe on-premises tier 104 includes an equipment unit 106 with edgecircuitry 108 (e.g., onboard circuitry, local circuitry) communicabledirectly with the cloud tier 102 and in particular with cloud system 110of the cloud tier 102 (e.g., via only network communicationsinfrastructure, internet architecture, without intervening supervisoryor field controllers, etc.). The cloud tier 102 is also shown asincluding cloud applications 112 and remote services 114, both or eitherwhich may be executed by or via cloud system 110, for example. A unifiedpane 116 is accessible via the cloud tier 102 and/or the on-premisestier 104 and provides visualizations of and user interactivity with thebuilding automation system 100 and data relating thereto. Theon-premises tier 104 is shown as including various data sources, shownas security sensor 118, fire alarm pull 120, and a temperature sensor122.

While FIG. 1 shows the security sensor 118, fire alarm pull 120, andtemperature sensor 122. In various embodiments, the data sources caninclude various sensors (security sensors, cameras, door sensors, smokedetectors, fire sensors, indoor temperatures sensors, pressure sensors,outdoor temperature sensors, humidity sensors, occupancy sensors, airquality sensors, flow meters, power meters, etc.), equipment (e.g.,rooftop units, chillers, air handling units, variable air volume boxes,etc.), or other devices or systems (e.g., building scheduling system,thermostats, nurse call system, etc.). The data sources can includelocal sources located at a same facility as the rooftop unit, internalsources at or proximate the rooftop unit, peer sources communicating ina peer-to-peer fashion with the onboard circuitry, and/or externalsources communicating directly with the onboard circuitry or only viadevices located at the same facility. The data sources can provide(e.g., stream substantially continuously, periodically transmit, etc.)signals (data, information, etc.) to the edge circuitry 108.

The equipment unit 106 and the edge circuitry 108 can share a housing109, for example with a heating, ventilation, or cooling component(e.g., compressor, evaporator, valve, actuator, fan, damper, coolingcoil, heating coil, etc.) of the equipment unit 106 and the edgecircuitry 108 both coupled to and/or enclosed within the housing 109. Insome embodiments, the equipment unit 106 and the edge circuitry 108 arepackaged together at a factory or warehouse and delivered as anintegrated package (e.g., coupled to a common pallet) to a building sitefor installation. In the example of FIG. 1 , the equipment unit 106 is arooftop unit. The equipment unit 106 may be one or more of various othertypes of building equipment in other embodiments (e.g., chiller, airhandling unit, variable air volume box, cooling tower, actuator, valve,air purifier, water heater, boiler, thermal energy storage, battery,variable refrigerant flow outdoor unit, variable refrigerant flow indoorunit, lighting device, controllable security device, controllable firesafety device, etc.). The equipment unit 106 is operable to affect avariable condition of a building (e.g., temperature, humidity, airflow,air quality, pressure, brightness, lighting color temperature, etc.).

As shown in FIG. 1 , the equipment unit 106 and the edge circuitry 108include a bridge communications layer 124 that enables communicationbetween the on-premises tier 104 and the cloud tier 102. The bridgecommunications layer 124 is configured to provide a bridge between theedge circuitry 108 and the cloud system 110, for example as described inU.S. Provisional Patent Application No. 63/296,078, filed Jan. 3, 2022,the entire disclosure of which is incorporated by reference herein. Theedge circuitry 108 can store a portion of a digital twin of a facilityserved by the equipment unit 106, for example a digital twin with eventenrichment and contextual information as described in U.S. applicationSer. No. 17/504,121 filed Oct. 18, 2021, the entire disclosure of whichis incorporated by reference herein. In some implementations, the bridgecommunications layer 124 may additionally or alternatively performcertain processing and/or storage, such as processing of remotelyprogrammed rules or artificial intelligence/machine learning routinesand/or storage of digital twins, on-premises, such as at the equipmentunit 106.

The cloud tier 102 is shown as including a cloud system 110 andassociated cloud applications 112 and remote services 114. The cloudapplications 112 can include fault prediction, detection, and diagnosticfeatures that predict, detect, and/or diagnose faults of the equipmentunit 106, and, in some embodiments, recommend maintenance orautomatically cause a change in control of the equipment unit 106 toprevent or mitigate such faults. As another example, the cloudapplications 112 can include optimization applications that performoptimizations configured to reduce utility costs, energy usage, carbonemissions, or some combination thereof, for example subject toconstraints that ensure occupant comfort, and provide control settings(e.g., zone temperature setpoints) to the equipment unit 106 as theoutput of such applications (e.g., for example, as described in U.S.Patent Publication No. 2020/0041158, filed Oct. 10, 2019 and/or U.S.patent application Ser. No. 17/668,791, filed Feb. 10, 2022, the entiredisclosures of which are incorporated by reference herein. As anotherexample, the cloud applications 112 can include sustainability tools formanaging pollution emissions (e.g., carbon emissions) and/or generatingcontrol settings for the equipment unit 106 to achieve an emissionstarget (e.g., net zero energy consumption), for example as described inU.S. Provisional Patent Application No. 63/301,910, filed Jan. 21, 2022,the entire disclosure of which is incorporated by reference herein. Insome implementations, the cloud applications 112 may include featuresrelating to assessing and improving occupant, space/building and/orenvironment health, indoor air quality, and/or infection risk, forexample as described in U.S. Provisional Patent Application No.63/230,608, filed Aug. 6, 2021, U.S. patent application Ser. No.17/459,963, filed Aug. 27, 2021, and U.S. Provisional Patent ApplicationNo. 63/281,409, filed Nov. 19, 2021, the entire disclosures of each ofwhich are incorporated herein by reference. While these features aredescribed as being optional parts of the cloud applications 112, itshould be understood that, in various implementations, aspects of thefeatures may additionally or alternatively be implemented as part of theon-premises tier 104, such as within the edge circuitry 108. Forexample, in some implementations, one or more of these or other featuresmay be implemented fully within the edge circuitry 108, and in someimplementations, a portion of the features may be implemented within theedge circuitry 108 and a portion may be implemented within the cloudapplications 112 (e.g., such that the edge circuitry 108 and the cloudapplications 112 work in concert to execute the features). The remoteservices 114 can enable expert access to data relating to the equipmentunit 106 and expert interventions relating to operation of the equipmentunit 106, for example.

In some implementations, the architecture shown in FIG. 1 may haveseveral advantages over a more conventional, multi-tiered buildingautomation system (BAS) architecture. A multi-tiered BAS architecturemay include, for example, edge devices such as rooftop units (RTUs),chillers, air handling units (AHUs), and the like, and may furtherinclude multiple layers of controllers to interface with such units. Forexample, in such an architecture, an edge device such as a RTU mayinterface with a field controller, which may be located proximate to orotherwise in direct communication with the RTU. The field controller mayin turn interface with a supervisory controller, which may interact withlocal, on-premises controls and/or cloud or other off-premises services.

In some implementations of the present disclosure, as illustrated inFIG. 1 , the edge devices, such as the RTU, may interface directly withcloud or other off-premises systems/services via the edge circuitry 108.Accordingly, some implementations of the present disclosure provide aflattened architecture relative to a multi-tiered architecture and may,for example, remove the need for one or more intervening controllers(e.g., field controllers, supervisory controllers, etc.). In someimplementations, some functions of such devices may be implemented inthe edge circuitry 108, the cloud applications 112, or a hybrid thereof.

Such an architecture can substantially reduce the time to install a newbuilding management system/building automation system (e.g., reducinginstallation and configuration time from weeks to hours, in someinstances). The architecture may support over the air updates and remoteserviceability through the cloud tier 102. The architecture may supporthigher-order analytics that may be performed at the cloud tier 102, theon-premises tier 104 (e.g., at the edge circuitry 108), or by a hybridcombination thereof. The architecture may allow for quicker and easierconfiguration of such analytics as well (e.g., reducing the time toonboard/activate particular analytics services, such as from weeks tohours in some cases). The architecture may support automatedconfiguration of some equipment and services. In some implementations,the architecture may reduce or eliminate the need for multiple,disjointed user interfaces, data models, and other tools and insteadallow for a unified set of tools/models/interfaces to be applied acrossa variety of equipment/spaces/buildings/applications/etc.

It should be understood that edge circuitry, as utilized herein, doesnot require that the described components/apparatus be separate anddistinct circuitry, such as separate circuitry from that of edge devicessuch as rooftop units or chillers. Rather, in various implementations,the edge circuitry or other circuitry described herein may beimplemented as separate hardware and/or software, integrated with or bea part of existing hardware/software of existing devices, such as edgedevices like rooftop units/chillers or other on-premises computingdevices such as servers or controllers, or a combination thereof. Insome implementations, the edge circuitry or other circuitry describedherein may be implemented as instructions stored on one or morecomputer-readable storage media, such as storage media of an existingdevice or on a separate storage medium, that are executable by one ormore processors (e.g., processors of existing equipment or otherprocessors) to implement functions described herein. In someimplementations, the instructions may be added to one or moreon-premises devices, such as by providing some or all of theinstructions to the devices during manufacturing or afterinstallation/during operation via in-person or remote programming of thedevices.

Referring now to FIG. 2 , an enterprise system 200 is shown, accordingto some embodiments. The enterprise system 200 can be characterized asan extension of the building automation system 100 of FIG. 1 formultiple facilities. Advantageously, the enterprise system 200 uses thesame architecture as the building automation system 100, with thearchitecture being agnostic to the count of the number of different offacilities included, the count of the number of different equipmentunits included, and of the size of a facility or multiple facilitiesincluded in the enterprise system 200. The architecture and otherfeatures disclosed herein are thus usable across buildings, campuses,and enterprises of different scales and complexities (an includinginternal differences in scale and complexity) without change inarchitecture. In the example shown, the enterprise system 200 includesthe cloud tier 102 and instances of the on-premises tier 104 at multiplebuildings (shown as three retail branches with on-premises tier 104 a,on-premises tier 104 b, and on-premises tier 104 c, respectively). Theenterprise system 200 also includes an instance of the on-premises tierfor a different type of building, shown as edge tier 202 for aheadquarters building.

In the example of FIG. 2 , the cloud tier 102 includes the cloud system110, the cloud applications 112, and the remote services 114, with useraccess via unified pane 116. The cloud tier 102 is also shown asincluding digital twin 204 and a third party cloud 206. The digital twin204 can be a digital twin as described in U.S. application Ser. No.17/504,121 filed Oct. 18, 2021, the entire disclosure of which isincorporated by reference herein. The third party cloud 206 can be anycloud system, resource, service, etc. which provides data useful to thecloud applications 112, remote services 114, or digital twin 204 and/oruses outputs of the cloud applications 112, remote services 114, digitaltwin 204, on-premises tiers 104 a-c, or edge tier 202 to provide variousfunctionality in various embodiments.

The edge tier 202 for a headquarters building is shown as including anequipment unit 208 which may be a different type of equipment thanequipment unit 106 of on-premises tier 104 as shown in FIG. 1 (and as inon-premises tiers 104 a-c in FIG. 2 in various examples). The edge tier202 may include multiple equipment units 208 in various embodiments. Forexample the equipment unit 106 for a retail branch may be an rooftopunit while a headquarters building may have other plant equipment as theone or more equipment units 208 (e.g., chiller(s), boiler(s), etc.), ormay have a more complex/larger rooftop unit or set of multiple rooftopunits. The edge tier 202 also includes edge circuitry 210 coupled to,enclosed with, packaged with, distributed with, integrated with, etc.the equipment unit 208. The edge circuitry 210 advantageously has thesame or similar design as edge circuitry 108 of FIG. 1 , for examplewith adaptations for use with the type of equipment of equipment unit208. Various data sources can be connected thereto as above withreference to FIG. 1 , with FIG. 2 showing that complex systems 212 suchas an on-premises system performance system, etc. (e.g., hosted on anon-premises server) can be included and communicate directly with theedge circuitry 210 (e.g., without first routing through the cloud system110) and/or with the cloud system 110. In some implementations, suchcomplex systems 212 may be integrated as a part of the edge circuitry210.

FIG. 2 is illustrative of the scalability of the architecture of thepresent disclosure across different facilities, campuses, enterprises,real estate portfolios, equipment distribution networks, service areas,etc., according to some embodiments. Although retail branches and aheadquarters is shown in the example shown, various other combinationsof different types of facilities are possible (e.g., residential,classroom, athletics, and laboratory facilities of a college campus;hospital, clinic, and pharmacy facilities of a medical group;storefronts, factories, and warehouses of a consumer goods business;hotels and corporate offices for hotel groups; stadiums and corporateoffices for the ownership groups; airport terminals and other airportoperations facilities and/or office spaces; etc.). The architecture canenable services appropriate for such facilities (e.g., usingthree-dimensional building models of the relevant buildings) withoutrequiring modification to the underlying architecture of the buildingautomation systems shown in FIGS. 1-2 .

Referring now to FIG. 3 , a detailed view of cloud tier 102interoperating with edge circuitry 108 is shown, according to someembodiments. The edge circuitry 108 is shown as including a dataingestion layer 300, an analytics layer 302, and a data publicationlayer 304. The cloud tier 102 is shown as including an analyticsmanagement portion 306 and a cloud processing portion 308. The analyticsmanagement portion 306 interoperates with the analytics layer 302 of theedge circuitry 108 while the cloud processing portion 308 interoperateswith the data publication layer 304.

The data ingestion layer 300 is configured to ingest data from multiplesources received from the sources in multiple data formats and usingmultiple data protocols, translate the data into a common data format,and provide the data in the common data format to a common data bus 310of the analytics layer 302. In some embodiments, the data ingestionlayer 300 and elements thereof can be implemented using features foringesting and processing streaming data and/or sets of data as describedin U.S. Pat. No. 10,007,513, filed Aug. 29, 2016, U.S. Pat. No.11,048,498, filed Aug. 13, 2019, U.S. Pat. No. 10,572,230, filed Mar.23, 2017, and/or U.S. Pat. No. 10,564,941, filed Mar. 23, 2017, thedisclosures of which are incorporated by reference herein in theirentireties. The common data format may be a Brick format, for example,or any other type of common data model. The data ingestion layer mayapply tags to the data, for example tags indicating types of theentities, relationships between the entities, for example location,event, asset, and place tags. The data ingestion layer 300 can alsoprovide various pre-processing steps, including normalizing, aligning(e.g., arranging data from multiple sources into discrete values at acommon frequency/time step interval), filtering, cleaning, etc. the datareceived at the data ingestion layer 300 before providing such data tothe data bus 310.

As shown in FIG. 3 , the data ingestion layer 300 includes multipleinputs 307 (ports, pins, wireless receivers, etc.) that receive signals(data, etc.) from sources 312 and provide such signals to an MQTT agent314, an OPCUA agent 316, a Modbus agent 318, a DDS agent 320, and aBACnet agent 322. The MQTT agent 314 is configured to translate datafrom a MQTT protocol to a common data format used by the data bus 310(e.g., data from an internet-of-things sensor). The OPCUA agent 316 isconfigured to translate data from a OPCUA protocol to the common dataformat. The Modbus agent 318 is configured to translate data (e.g., froma building sensor) from a Modbus protocol to the common data format. TheDDS agent 320 is configured to translate data from a DDS protocol to thecommon data format. The BACnet agent 322 is configured to translate thedata (e.g., internal data of the unit of building equipment, data fromother building equipment) from a BACnet protocol to the common dataformat. The agents 314-322 can be selectively included and excludeddepending on the data protocols of the data sources communicably coupledto the edge circuitry 108, including in some examples by adding an agentfor a new protocol via an over-the-air update when a data source usingthe new protocol is connected. The agents 314-322 can translate the datain real time (e.g., in substantially continuous streams) so thatreal-time data is provided onto the data bus 310. Such local datatranslation avoids latency issues which may delay data processing inalternative embodiments where such data translations are performed at anoff-premises server. While the agents 314-322 can in someimplementations be implemented using software agents, it should beunderstood that, in other implementations, the protocolbrokers/translation layers may be implemented using methods other thansoftware agents.

The analytics layer 302 is configured to execute one or more of multipletypes of logic, including control logic (e.g., a PID feedback controlloop), expression-based event processing and/or pattern recognition,and/or one or more machine learning or artificial intelligencealgorithms/routines (e.g., a machine learning algorithm specificallymodified to have a smaller memory footprint thereby enabling edgeexecution). Such logic is performed using data in a common data formatfrom data bus 310 and can include sending control signals to theequipment unit 106 (i.e., to electromechanical components that operatein accordance with such control signals to affect a condition of abuilding) or transmitting results to the cloud tier 102 via a cloudconnector 323 of the data publication layer 304.

The analytics layer 302 is shown as including the data bus 310, edgemanager 324, configurator 326, metrics 328, analytic expression domainspecific language 330, analytics engine 332, software development kit334, product applications 336, and other applications 338. The data bus310, edge manager 324, configurator 326, metrics 328, analyticexpression domain specific language 330, analytics engine 332, andsoftware development kit 334 are shown as exchanging information withthe data bus 310, while the other applications 338 and the productapplications 336 interoperate with the data bus 310 via the softwaredevelopment kit 334 in the illustration shown.

The edge manager 324 interoperates with a cloud manager 340 of theanalytics management portion 306 of the cloud tier 102. The cloudmanager 340 provides information and receives inputs from a userinterface console 342 (e.g., a browser-based interface hosted by thecloud manager 340 and accessible via the Internet from a personcomputing device). The cloud manager 340 and the user interface console342 interact with an access management system 344 which determineswhether a user has authority to manage the edge circuitry 108 (e.g.,based on login credentials, etc.) and, in response to determining thatthe user has authority to manage the edge circuitry 108, allowing theuser to access the user interface console 342 and to interact with thecloud manager 340. An example interface displayed by the user interfaceconsole 342 and providing interactions with the cloud manager 340 tomanage analytics executed by the analytics layer 302 is shown in FIG. 4and described with reference thereto below. New or updatedexpression-based logic can be transmitted remotely to the edge circuitry108 to enable over the air updates of the edge circuitry 108 and, insome scenarios, other similar edge circuitry for similar edge devices ina network.

The cloud manager 340 provides for creation of and modifications tovarious logic executed by the analytics layer 302. As one example, thecloud manager 340 allows a user (via user interface console 342) toselect or create expression-based logic for execution by the analyticslayer 302. For example, the cloud manager 340 may provide tools andmethods for a real-time data flow programming language as described inU.S. Pat. No. 10,977,0101, filed Apr. 21, 2020, and/or U.S. Pat. No.10,127,022, filed Mar. 23, 2017, the entire disclosures of which areincorporated by reference herein. The expression-based logic may enablecomplex event processing that can perform real-time analysis ofdisparate streams of data (e.g., collected on data bus 310),simultaneously perform complex pattern recognition on high frequency andasynchronous streaming data, detect events in real time (enablingimmediate response such as closed loop control actions), and handlemachine learning pre- and post-processing. For example, theexpression-based logic may be selected or customized via the cloudmanager 340 to define fault diagnosis rules based on trends in data onthe data bus 310 (e.g., comparing rates of change of different variablesfrom different data sources). Such expression-based logic can be storedat analytic expression DSL 330 and executed by analytics engine 332 ofthe analytics layer 302 of the edge circuitry 108.

As another example, the cloud manager 340 is configured to train aneural network (or other machine learning or artificial intelligencemodel), for example on historical data of configuration, events,performance, etc. of the equipment unit 106 and/or other equipment units(e.g., similar equipment units serving similar buildings). The cloudmanager 340 may provide the trained neural network to the edge manager324. In some embodiments, the cloud manager 340 modifies the trainedmodel in a manner that reduces the memory and computing resources neededto run an algorithm using the model, and provides the modified model tothe edge circuitry 108. The model can be edge-converted (“edge-ified”)as described in U.S. Patent Publication No. 2020/0327371, filed Apr. 9,2019, the entire disclosure of which is incorporated by referenceherein. The modified (edge-converted, edge-ified, etc.) model may beusable by the edge circuitry 108 use continuous streams of data asinputs from the data bus 310 and produce inferences (predictions,diagnoses, control outputs) without communication to the cloud tier 102.The cloud manager 340 can periodically update the edge-converted modelin a closed-loop manner by interoperating with the edge manager 324, forexample. The edge-converted model can be stored by the edge manager 324on the edge circuitry 108 and used in one or machine learningalgorithms, for example executed by the analytics engine 332 of theanalytics layer 302. In some embodiments, the edge-converted model isprovided onto data bus 310 so that it can be used by apps 338 andproduct apps 336 via SDK 334.

The cloud manager 340 and user interface console 342 can also enablevarious other automated or user-selected adjustments of settings andcontrol logic, for example. For example, a user may select temperaturesetpoints, desired temperature ranges, preferences for comfort versuscosts or energy or carbon savings, etc. which may be used by variouscontrol logic (e.g., PID feedback controller, extremum seekingcontroller, etc.), analytics, or model-based processes (e.g., modelpredictive control, predictive maintenance, etc.) performed by the edgecircuitry 108.

Configurator 326 of the analytics layer 302 is configured toautomatically determine a configuration for the edge circuitry 108 andthe equipment unit 106. The configuration can include multipleparameters that tune the edge circuitry 108 and the equipment unit 106to or toward ideal performance. In some embodiments, the configurator326 uses expression-based event processing logic to assess data from thedata bus 310 and uses results of such expression-based event processinglogic to determine configuration parameters. In some embodiments, theconfigurator 326 uses a machine learning model (e.g., an edge-convertedmachine learning model, trained on historical configurations of similarequipment units) to determine a configuration. In some embodiments, theconfigurator 326 interoperates with the cloud manager 340 to determinethe configuration in a hybrid cloud/edge manner, for example with theconfigurator 326 and the cloud manager 340 determining different subsetsof configuration parameters. In some embodiments, the configurator 326and/or the cloud manager 340 (e.g., in coordination with the userinterface console 342) perform operations for automatic configuration asdescribed in U.S. Pat. No. 11,272,011, filed May 19, 2021, the entiredisclosure of which is incorporated by reference herein.

As shown in FIG. 3 , the analytics layer 302 is dockerized such thatvarious apps 338 and product apps 336, (and analytic expressions,machine learning models, etc.) can be modularly added or removed fromthe analytics layer 302, for example via over-the-air updates. The apps338 and product apps 336 can include various control logic for theequipment unit 106, for example. The apps 338 and the product apps 336can include various other programs, analytics, metrics calculators,visualization generators, etc. that enable various capabilities for theequipment unit 106.

The edge circuitry 108 is further shown as including the datapublication layer 304. The data publication layer 304 includes cloudconnector 323 and CEG HW 339. The cloud connector 323 is configured toprovide a bridge between the edge circuitry 108 (e.g., the data bus 310)and the cloud tier 102 (e.g., the cloud processing portion 308), forexample as described in U.S. Provisional Patent Application No.63/296,078, filed Jan. 3, 2022, the entire disclosure of which isincorporated by reference herein. The CEG HW 339 provides for dataupdates to and from the cloud tier 102, for example via SDK 334.

The cloud processing portion 308 of the cloud tier is shown as includingan event processor 346, a message pipeline/storage 348, and enterpriseapplications 350. The event processor 346 may be configured to receivedata and analytics outputs from the edge circuitry 108 and store suchoutputs. The event processor 346 may also be configured to performadditional (e.g., higher-level) analytics and processing of suchinformation to generate additional insights and actionable steps orrecommendations relating to the equipment unit 106. The messagepipeline/storage 348 provides for communication between the eventprocessor 346 and enterprise applications 350. The enterpriseapplications 350 can include various cloud-based capabilities associatedwith managing, tracking, and/or affecting operation of the equipmentunit 106 and, in some scenarios other building equipment communicablewith the cloud tier 102. For example the enterprise applications 350 mayprovide a distributor dashboard enabling comparison of equipmentperformance, events, etc. across many units of equipment, differentfacilities, different customers, different equipment owners, differenttechnicians or sales representatives, etc. As another example, theenterprise applications 350 can provide a user interface (e.g., via amobile application, via a webpage hosted by enterprise applications 350,etc.) that enables a user to view events, faults, etc. for the equipmentunit 106 (e.g., as shown in FIG. 5 and described with reference theretobelow) or a fleet of equipment as shown in FIGS. 6-7 .

Referring now to FIG. 4 , a view of an interface 400 provide userinterface console 342 is shown, according to some embodiments. In theexample shown, the interface 400 provided by the user interface console342 is a webpage hosted by the analytics management portion 306 of thecloud system and shown as being accessed by and displayed on a personalcomputing device (e.g., laptop computer, desktop computer, etc.). Insome embodiments, the interface 400 is provided on unified pane 116.

The interface 400 includes a menu 402 that includes buttons that enablea user to navigate to an edge device (e.g., edge circuitry 108) of a setof possible edge devices that can be managed by the analytics managementportion 306 (e.g., multiple edge devices for a building site orportfolio). The menu 402 can include filtering and search features. Insome embodiments, multiple edge devices (e.g., all edge devices of aselected equipment type) can be selected together and managed together.

The interface 400 also includes a tabs bar 404. The tabs bar 404 allowsa user to select to view different information and different manageablefeatures for a selected edge device. As shown, the tabs bar 404 includesselectable tabs for health, status, edge details, and solutions whichshow information about the edge devices. The tabs bar 404 also listssensors, analytics, edge machine learning (ML), apps, and datapublications which may correspond to views that providecustomizable/manageable features of the device. In the example shown, ananalytics tab 406 is selected from the tabs bar 404.

The analytics tab 406 is shown as including a list 408 of analytics(and/or other operations) available for execution by the edge device. Asshown, the analytics may include data ingestion and tagging features,for example executable by the data ingestion layer 300 of the edgecircuitry 108. The analytics are also show as including alarms and eventprocessing that can be executed by analytics engine 332 of the edgecircuitry 108 using expression-based programming, for example a setpoint delta transformation and alarming expression-based program. Theanalytics tab 406 also includes a column 410 indicating whether eachitem on the list 408 is enabled on the edge device (as shown, all listeditems are enabled).

The analytics tab 406 also includes an add button 412. The add button412 is selectable to add one or more additional analytics. The addedanalytics can be preprogrammed expressions in an expression-basedlanguage, for example, thereby allowing a user to select from a set ofexpert-created and validated expression-based logic. The analytics canalso be user-created, for example via an expression language studiointerface accessible by selecting a launch button 414 of the analyticstab 406. The studio interface may provide an intuitive experience forcreating logic in an expression-based language for execution by the edgecircuitry 108, in some examples without requiring software programmingexpertise. For example, the interface 400 may provide a developmentenvironment and programming language as described in U.S. Pat. No.10,977,010, filed Apr. 21, 2020, the entire disclosure of which isincorporated by reference herein.

The analytics tab 406 thereby provides a user with options to remotelyselect, unselect, and customize logic to be executed by the edgecircuitry 108. The logic executed by the edge circuitry 108 can thus beeasily modified and updated remotely via the cloud manager 340. In someembodiments, many instances of the edge circuitry 108 (e.g., formultiple units of the same time of equipment installed at a facility ormultiple facilities) can be updated together using the analytics tab406, thereby enabling over-the-air customization of a fleet of equipmentunits.

Referring now to FIG. 5 , an events interface 500 is shown, according tosome embodiments. The events interface 500 can be provided on unifiedpane 116, for example. The events interface provides a list of eventswhich occurred for a particular building or space. As shown, the eventsinterface 500 shows a list of events detected locally at the equipmentunit 106 by edge circuitry 108 executing expression-based patternrecognition logic activated via interface 400. The edge circuitry 108can locally determine the occurrence of such events and provideinformation indicating that an event occurred to the cloud tier 102without all data necessary to detect such an event uploaded to the cloudtier 102. Such an architecture can save bandwidth and cloud storagerequirements, for example. The events interface can then provide a list502 of such events and a details area 504 showing further details of thecontextual data provided event notifications from the edge circuitry 108(e.g., time stamp, event type, relevant points, etc.). The event datacan also be provide in various other interfaces of a building automationsystem, for example integrated alongside building performance data andoptions for remotely controlling building equipment.

Referring now to FIGS. 6-7 , dashboards for viewing data for a fleet ofequipment units, for example data provided from edge circuitry of saidfleet of equipment units, are shown, according to some embodiments. Thedashboard 1800 of FIG. 6 and the dashboard 2000 of FIG. 7 can beprovided on unified pane 116, for example. The dashboards 1800, 2000 maybe provided as part of enterprise applications 350 as shown in FIG. 3 ,for example.

Advantageously, the dashboards provide aggregated data for multipleequipment units for different building sites and, in some examples,equipment units owned or leased by different customers or buildingowners. The dashboards thereby enable a service branch, distributor,sales representative, technician, manufacturer expert, etc. to reviewperformance across customers and sites, identify trends or outliners,determine areas or customers for updates, upgrades, maintenance, etc.,and otherwise more easily manage large fleets of building equipment.

Referring particularly to FIG. 6 , a dashboard 1800 is shown, accordingto some embodiments. The dashboard 1800 shows a monthly comparisonwidget 1802, a country graph widget 1804, a map widget 1806, a branchwidget 1808, a country selection widget 1810, and a month selectionwidget 1812 arranged to be displayed simultaneously on a display screenof a user device (e.g., via unified pane 116).

The monthly comparison widget 1802 shows a total number of active units(e.g., rooftop units, chillers, other types of equipment) with aperformance score (shown as a connected equipment performance index asdescribed in U.S. Pat. No. 11,092,954, field Jan. 10, 2019, the entiredisclosure of which is incorporated by reference herein) of less than athreshold value (shown as less than 50). In some examples, theperformance scores is calculated locally on each unit of equipment, forexample using expression-based analytics and/or machine learningalgorithms. Performance scores below the threshold value can beconsidered as poorly performing, in need of control adjustments, in needof maintenance, or otherwise in need of intervention. The monthlycomparison widget 1802 can be generated by processing the aggregateddata from step 1704 and counting, for each month period a number ofdifferent devices associated with connected equipment performanceindices for that month less than the threshold value and then displayingthose total numbers as a bar graph as shown in FIG. 18 . The monthlycomparison widget 1802 can show a user general trends in how a fleet ofconnected equipment is degrading (increasing the number of poorperforming units) and/or being serviced or better operated (decreasingthe number of poor performing units) over time, e.g., over a period oftwo years on a month-to-month basis as in the example of FIG. 6 .

The country widget 1804 displays a bar graph of the connected equipmentperformance index associated with each of multiple countries. Othergeographic distinctions are included in other examples (neighborhoods,campuses, cities, counties, states, etc.). For example, the value foreach country may be an average of all performance scores for all of theunits of connected equipment in the particular country (or a median,etc. in other embodiments). The country widget 1804 may arrange thecountries in order from worst (e.g., lowest) score to best (e.g.,highest) score, so that a user can easily see which region has theworst-performing connected equipment. Although the example shows scoresby country, other geographic categorizations can be used in variousembodiments (states, territories, counties, states, regions, cities,neighborhoods, campuses, etc.). The country widget 1804 can allow a userto determine where to focus attention for improvements, maintenance, andother interventions.

The map widget 1806 shows similar data as the country widget 1804visualized in a map view. In particular, the map widget 1806 shows a map(shown as a world map, but may be a map of a smaller region in otherembodiments) which data visualized on the map to show connectedequipment performance index values for different geographic regionsshown on the map. In the example shown, each country in which connectedequipment is located is provided with a circle (e.g., colored and/orshaded circle) which is sized and/or colored based on an average orother aggregate performance score associated with that country. In someembodiments, a larger circles indicates better scores while smallercircles indicate lower scores (or vice versa in other embodiments). Insome embodiments, each country has a circle sized based on a number ofunits of connected equipment located in that country while the circlesare colored based on performance index values (e.g., green for good/highvalues, yellow for moderate values, red for bad/low values). The mapwidget 1806 thus shows a graphical view of equipment performance acrossgeographic areas.

The branch widget 1808 shows a graph of performance scores (e.g.,connected equipment performance indices) for different branches, i.e.,for different business units, departments, subgroups, subsidiaries,customers, service technicians, sales representatives, etc. associatedwith sets of connected equipment. As shown in FIG. 6 , the branch widget1808 shows a bar graph with a bar for each different branch, or at leastfor a subset of all different branches included in a given scenario(e.g., for the branches with the worst five scores). The branch widget1808 may order the graph so that the worst branch (i.e., with theworst/lowest score) is shown first, enabling a user to easily see thebranch which needs the most intervention, attention, maintenance,investment, etc. based on the aggregated data visualized on dashboard1800.

The country selection widget 1810 and the month selection widget 1812are configured to enable a user to reduce the amount of data displayedon the dashboard 1800. The month selection widget 1810 allows a user toselect a month or subset of months for which the dashboard 1800 willdisplay data and visualizations. For example, if a user selects a fewmonths from a set of available months, the monthly comparison widget1802, the country graph widget 1804, the map widget 1806, and the branchwidget 1808 will update so that the monthly comparison widget 1802, thecountry graph widget 1804, the map widget 1806, and the branch widget1808 visualizes data for the selected months. Other time periods (years,seasons, days of the week, particular dates, parts of days, hours, etc.)could be selectable in the same manner in various embodiments.

The country selection widget 1810 provides a button for each countryincluded in the data and allows a user to select the countries for whichdata is desired to be displayed on the dashboard 1800. Other types ofgeographic areas (regions, states, territories, counties, cities, etc.)can be similarly selectable in other embodiments. In the example shown,if a user selects a subset of countries, the monthly comparison widget1802, the country graph widget 1804, the map widget 1806, and the branchwidget 1808 will update so that the monthly comparison widget 1802, thecountry graph widget 1804, the map widget 1806, and the branch widget1808 visualizes data for the selected countries.

Referring now to FIG. 7 , a dashboard 2000 of equipment fleet data isshown, in particular including a visualization of data from a selectedone-month period. A user may be enabled to navigate to the dashboard2000 via the dashboard 1800 of FIG. 6 . The dashboard 2000 includes anaverage score widget 2002, an index buckets widget 2004, a timelinewidget 2006, and events widget 2008, a field selection widget 2010, anda score filter widget 2012.

The average score widget 2002 is configured to show an averageperformance score for the subset of data represented in the selected(filtered) dataset. The index buckets widget 2004 shows the number offaults and the number of occurrences corresponding to equipmentperformance scores in different ranges (shown as greater than 75,between 50 and 75, and less than 50). The faults, performance scores,etc. can be calculated at the edge and then aggregated at the cloud fordisplay via the dashboard 2000, thereby reducing bandwidth on networkcommunications and resource demand on a cloud system that would bepresent in an embodiment where all data is uploaded to the cloud andprocessed there to identify faults and calculate performance scores.

The timeline widget 2006 is configured to show a bar chart of connectedequipment performance scores for each day in the selected month,spatially arranged in temporal order. The bar chart is overlaid with aline chart representing an average penalty value for each day. Thetimeline widget 2006 thereby shows a performance score and a penaltyvalue for each day, for example so that a user could easily and quicklysee any trends which occurred over the course of the selected month.

The events widget 2008 is configured to show events which occur relatingto the connected equipment in the selected month (or satisfying otherfilter criteria). Events may include detected faults, alarms, or othernotable conditions or events relating to the connected equipment. Theevents widget 2008 can list the date, entity, facility, particularequipment asset, model number, serial number, penalty value, penaltytype, and description for each event, for example. Events can bedetermined at the edge by the equipment using complex expression-basedevent processing, for example.

The field selection widget 2010 is configured to present lists ofcategorizations from which the user can select particular filters tofurther apply to the data used to generate the dashboard 2000. Forexample, the field selection widget 2010 is shown as including acustomer list (allowing selection of one or more customers or otherentities), a facility list (allowing selection of one or more particularfacilities), and an asset name list (allowing selection of particularequipment assets). Once one or more additional fields are selected by auser via the field selection widget 2010, the dashboard 2000 updates sothat the widgets 2002-2008 visualize data corresponding only to theselected fields. A user is thereby enabled to select the particulardataset(s) the user wishes to see visualized on the dashboard 2000.

The score filter widget 2012 is configured to accept a request to updatethe dashboard 2000 to only visualize data corresponding to performancescores in a user-selectable range. FIG. 20 shows the score filter widget2012 set to show scores between 0 and 100, with the upper value and thelower value adjustable by numerical input or by digital manipulation ofa slider feature. For example, if a user resets the range shown in scorefilter widget 2012 to scores between thirty and 70, the widgets2002-2008 will update to only show data corresponding to such datapoints. As one example, the events widget 2008 will be updated to onlyshow events which occurred while performance was scored in the selectedrange. The dashboard 2000 thereby enables yet another way to sort andfilter the displayed data.

The dashboard 1800 and the dashboard 2000 thereby provide various waysof visualizing and understanding advance performance information fromunits of building equipment spread across buildings, geography, endusers, technicians, etc. Such dashboards can be enabled in a seamlessmanner by performing the advanced event processing and performancescoring at the edge for each unit of equipment, and then aggregatingthat higher-level information at the cloud tier 102 for display to auser. Efficient and reliable presentation of the dashboard 1800 and thedashboard 2000, with little or no manual configuration, is therebyenabled by the present disclosure.

The hardware and data processing components used to implement thevarious processes, operations, illustrative logics, logical blocks,modules and circuits described in connection with the embodimentsdisclosed herein may be implemented or performed with a general purposesingle- or multi-chip processor, a digital signal processor (DSP), anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), or other programmable logic device, discrete gate ortransistor logic, discrete hardware components, or any combinationthereof designed to perform the functions described herein. A generalpurpose processor may be a microprocessor, or, any conventionalprocessor, controller, microcontroller, or state machine. A processoralso may be implemented as a combination of computing devices, such as acombination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration. In some embodiments, particularprocesses and methods may be performed by circuitry that is specific toa given function. The memory (e.g., memory, memory unit, storage device)may include one or more devices (e.g., RAM, ROM, Flash memory, hard diskstorage) for storing data and/or computer code for completing orfacilitating the various processes, layers and modules described in thepresent disclosure. The memory may be or include volatile memory ornon-volatile memory, and may include database components, object codecomponents, script components, or any other type of informationstructure for supporting the various activities and informationstructures described in the present disclosure. According to anexemplary embodiment, the memory is communicably connected to theprocessor via a processing circuit and includes computer code forexecuting (e.g., by the processing circuit or the processor) the one ormore processes described herein.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure may be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, orother optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures and description may illustrate a specific order ofmethod steps, the order of such steps may differ from what is depictedand described, unless specified differently above. Also, two or moresteps may be performed concurrently or with partial concurrence, unlessspecified differently above. Such variation may depend, for example, onthe software and hardware systems chosen and on designer choice. Allsuch variations are within the scope of the disclosure. Likewise,software implementations of the described methods could be accomplishedwith standard programming techniques with rule-based logic and otherlogic to accomplish the various connection steps, processing steps,comparison steps, and decision steps.

What is claimed is:
 1. A rooftop unit comprising: a housing; airconditioning components coupled to the housing; and circuitry enclosedwithin and/or coupled to the housing and programmed to execute a controllogic for the air conditioning components, an expression-based eventprocessing logic, and a machine learning algorithm.
 2. The rooftop unitof claim 1, wherein the expression-based event processing logic performspattern recognition for data received by the circuitry from the airconditioning components or one or more external data sources.
 3. Therooftop unit of claim 1, wherein the machine learning algorithm is basedon a machine learning model trained at a cloud system remote from therooftop unit.
 4. The rooftop unit of claim 3, wherein the machinelearning algorithm comprises a modified version of the machine learningmodel trained at the cloud system that is configured to execute on morelimited processing resources of the circuitry relative to the cloudsystem.
 5. The rooftop unit of claim 1, wherein the expression-basedevent processing logic diagnoses occurring fault conditions and themachine learning algorithm predicts future fault conditions.
 6. Therooftop unit of claim 1, wherein the circuitry is programmed to modifythe expression-based event processing logic in response to remoteupdates received at the circuitry.
 7. The rooftop unit of claim 1,wherein the expression-based event processing logic and the machinelearning algorithm have a combined memory footprint of less than 256 MB.8. The rooftop unit of claim 1, wherein the circuitry receives a firstdata set from the air conditioning components and a second data set froman external sensor, wherein the machine learning algorithm uses thefirst data set and the second data set as inputs.
 9. The rooftop unit ofclaim 8, wherein the external sensor is an indoor air quality sensor.10. The rooftop unit of claim 1, wherein: the circuitry is configured toestablished communications with a cloud system; and the control logicfor the air conditioning components, the expression-based eventprocessing logic, and the machine learning algorithm are functionalduring interruptions of the communications with the cloud system. 11.The rooftop unit of claim 1, wherein the control logic for the airconditioning components is a native control logic, and wherein theexpression-based event processing logic comprises a supplement ormodification of the native control logic.
 12. The rooftop unit of claim11, wherein the supplement or modification of the native control logicis received from a cloud system or another computing system externalfrom the rooftop unit via a network connection.
 13. The rooftop unit ofclaim 12, wherein the supplement or modification of the native controllogic is received after installation of the rooftop unit while therooftop unit is connected to the network connection and operational. 14.The rooftop unit of claim 1, wherein the control logic comprises a firstset of one or more fault detection and/or diagnostics rules, and whereinthe expression-based event processing logic comprises a second set ofone or more fault detection and/or diagnostics rules that supplement ormodify the first set of one or more fault detection and/or diagnosticsrules, the second set of one or more fault detection and/or diagnosticsrules received from a cloud system or another source remote from therooftop unit and defined according to an expression-based language. 15.A unit of building equipment comprising: a mechanical componentcontrollable to affect a condition of a building; and circuitry packagedwith the mechanical component and programmed to execute a control logicfor the heating, ventilation, or cooling component, an expression-basedevent processing logic, and a machine learning algorithm.
 16. The unitof building equipment of claim 15, wherein the expression-based eventprocessing logic performs pattern recognition for data received by thecircuitry from the mechanical component or one or more external datasources.
 17. The unit of building equipment of claim 15, wherein themachine learning algorithm is based on a machine learning model trainedat a cloud system remote from the unit of building equipment.
 18. Theunit of building equipment of claim 17, wherein the machine learningalgorithm comprises a modified version of the machine learning modeltrained at the cloud system that is configured to execute on morelimited processing resources of the circuitry relative to the cloudsystem.
 19. The unit of building equipment of claim 15, wherein theexpression-based event processing logic diagnoses occurring faultconditions and the machine learning algorithm predicts future faultconditions.
 20. The unit of building equipment of claim 15, wherein thecircuitry is programmed to modify the expression-based event processinglogic in response to remote updates received at the circuitry.
 21. Theunit of building equipment of claim 15, wherein the expression-basedevent processing logic and the machine learning algorithm have acombined memory footprint of less than 256 MB.
 22. The unit of buildingequipment of claim 15, wherein the circuitry receives a first data setfrom the mechanical component and a second data set from an externalsensor, wherein the machine learning algorithm uses the first data setand the second data set as inputs.
 23. The unit of building equipment ofclaim 22, wherein the external sensor is an indoor air quality sensor.24. The unit of building equipment of claim 15, wherein: the circuitryis configured to established communications with a cloud system; and thecontrol logic for the mechanical component, the expression-based eventprocessing logic, and the machine learning algorithm are functionalduring interruptions of the communications with the cloud system. 25.The unit of building equipment of claim 15, wherein the control logicfor the heating, ventilation, or cooling component is a native controllogic, and wherein the expression-based event processing logic comprisesa supplement or modification of the native control logic.
 26. The unitof building equipment of claim 25, wherein the supplement ormodification of the native control logic is received from a cloud systemor another computing system external from the unit via a networkconnection.
 27. The unit of building equipment of claim 26, wherein thesupplement or modification of the native control logic is received afterinstallation of the unit while the unit is connected to the networkconnection and operational.
 28. The unit of building equipment of claim15, wherein the control logic comprises a first set of one or more faultdetection and/or diagnostics rules, and wherein the expression-basedevent processing logic comprises a second set of one or more faultdetection and/or diagnostics rules that supplement or modify the firstset of one or more fault detection and/or diagnostics rules, the secondset of one or more fault detection and/or diagnostics rules receivedfrom a cloud system or another source remote from the unit and definedaccording to an expression-based language.
 29. A system comprising: aunit of building equipment comprising: a heating, ventilation, orcooling component; onboard circuitry configured to execute a controllogic for the heating, ventilation, or cooling component, anexpression-based event processing logic, and a machine learningalgorithm; and a cloud system communicably connectable to the onboardcircuitry and comprising circuitry configured to: transmit an expressionto the onboard circuitry for use by the expression-based eventprocessing logic; and transmit a machine learning model to the onboardcircuitry for use by the machine learning algorithm.
 30. The system ofclaim 29, wherein the expression-based event processing logic performspattern recognition for data received at the onboard circuitry from theheating, ventilation, or cooling component or an external data source.31. The system of claim 29, wherein the cloud system is configured togenerate the machine learning model by training a neural network on atraining data set comprising historical data from at least one of theunit of building equipment or other building equipment and generatingthe machine learning model to transmit to the onboard circuitry usingthe neural network.
 32. The system of claim 29, wherein theexpression-based event processing logic diagnoses occurring faultconditions and the machine learning algorithm predicts future faultconditions.
 33. The system of claim 29, wherein the expression-basedevent processing logic and the machine learning algorithm have acombined memory footprint of less than 256 MB.
 34. The system of claim29, further comprising a plurality of external data sources providing aplurality of data streams to the onboard circuitry, wherein theexpression-based event processing logic and the machine learningalgorithm are adapted to use the plurality of data streams as inputs.35. The system of claim 29, wherein the control logic, theexpression-based event processing logic, and the machine learningalgorithm are fully functional during interruptions of a connectionbetween the onboard circuitry and the cloud system.
 36. A methodcomprising: providing a package comprising a heating, ventilation, orcooling component and onboard circuitry; executing, by the onboardcircuitry, control logic to control the heating, ventilation, or coolingcomponent; executing, by the onboard circuitry, an expression-basedevent processing logic; and executing, by the onboard circuitry, amachine learning algorithm.
 37. The method of claim 36, wherein themethod further comprises: training, by a computing system remote fromthe onboard circuitry, a neural network on a training data set compriseshistorical data from at least one of the heating, ventilation, orcooling component or other heating, ventilation, or cooling components;and generating the machine learning algorithm to transmit to the onboardcircuitry using the neural network.
 38. The method of claim 36, whereinexecuting the expression-based event processing logic providesrecognition of patterns in data received at the onboard circuitry fromthe heating, ventilation, or cooling component or another data source.39. The method of claim 36, wherein executing the expression-based eventprocessing logic diagnoses an occurring fault condition and whereinexecuting the machine learning algorithm predicts a future faultcondition.
 40. The method of claim 36, further comprising receiving, atthe onboard circuitry and from a cloud system, a set of expressions anda machine learning model; wherein executing the expression-based eventprocessing logic comprises using the set of expressions and executingthe machine learning model comprises using the machine learning model.